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Machine learning is one possible application of artificial intelligence. It uses specific algorithms to teach machines how to learn, automatically improving performance and delivery.
This idea has proven to give humans incredible power. With machine learning, tasks can be run automatically, thus making life more comfortable.
What are the Best Books on Machine Learning to read?
Best Books on Machine Learning: Our Top 7 Picks
As a potential key to unraveling a new window of possibilities, it is imperative that you grasp the fundamentals of machine learning. There are tons of books and papers available on the subject, but it is always important to pick the best one for you.
Finding the right book can be quite difficult so to help you out, we’ve rounded up a list of our favorites:
1. The Hundred-Page Machine Learning Book
The Hundred-Page Machine Learning Book by Andriy Burkov will help you to easily learn machine learning through self-study within a few days.
The great thing about this book is that you don’t need to have any prior knowledge of the subject. As a novice, the first five chapters will guide you through learning the fundamentals, followed by chapters that teach you more advanced concepts in an easy to understand manner.
It is a great tool in the hands of students of data science. The book will also do those seeking in-depth knowledge about machine learning some good. If you have some basic knowledge about statistics, math, and probability, then you’ll be soaring through this book easily.
Realistically, you wouldn’t learn everything about machine learning from this book. However, you will all learn all that you need to know. Each chapter is written in such a way that the knowledge is broken down for easy understanding.
Here’s a simple tip. The counterfeit of this book is available and if you are not careful, you might just order it. To order the original, make sure it ships from Amazon directly.
- Author: Andriy Burkov
- Publisher: Andriy Burkov (January 13, 2019)
2. Hands-On Machine Learning with Scikit-Learn and TensorFlow
Deep learning has been instrumental in the improvement of machine learning. Hands-On Machine Learning with Scikit-Learn and TensorFlow uses this as a framework to help students understand the subject.
The idea is to help programmers who have no previous experience with the technology create their own programs by presenting them with simple yet efficient tools in the most practical manner.
The author helps the reader gain an intuitive understanding of tools and concepts used in developing these intelligent systems by employing minimal theory, concrete examples, and a dual, production-ready Python framework.
In a nutshell, the book provides readers with:
- An exploration of the landscape of machine learning and neural nets.
- Tracking of a sample machine learning project using scikit-learn.
- Experience with building and training of neural nets using the TensorFlow library.
- Techniques for scaling and training deep neural nets.
- Practical code examples.
Each chapter helps the reader practice they have learned. Readers have recommended this book as one of the best on machine learning because of the clarity of the language and wide range of topics covered.
- Author: Aurelien Geron
- Publisher: O’Reilly Media; 1 Edition (April 9, 2017)
- Pages: 574 pages
3. Fundamentals of Machine Learning for Predictive Data Analytics
Fundamentals of Machine Learning for Predictive Data Analytics is erudite but simple in its approach. It gives the reader a comprehensive introduction to some of the most used approaches in machine learning. These approaches are employed in predictive data analytics.
This book provides the reader with practical applications, accompanied by theoretical concepts. It excellently describes methods using analytics but the greatest value is in the practical examples.
The case studies make use of real-world situations and how predictive analytics can be used to solve these challenges. This textbook is written in clear terms, helping the reader to gain an intuitive understanding of machine learning.
Reading this book will introduce the reader to four approaches to machine learning, including:
- Information-based learning
- Similarity-based learning
- Probability-based learning
- Error-based learning
Each approach is explained using non-technical language, followed by illustrated algorithms and mathematical models in detailed examples. The final part of the book introduces the reader via two case studies to techniques for evaluating prediction models.
The book is fit for use by undergrads in computer science, mathematics, engineering, and statistics. Graduate students and professionals can also make use of the book for reference purposes.
It comes highly recommended by readers for its simple language and practical examples. Most, however, believe more advanced knowledge would make the book more relevant.
- Author: John D. Kelleher
- Publisher: The MIT Press; 1 Edition (July 24, 2015)
- Pages: 624 pages
4. Machine Learning: A Probabilistic Perspective
In search of a textbook that teaches probabilistic methods along with inference? Machine Learning: A Probabilistic Perspective is one of your best options, combining inference with probabilistic methods to comprehensively introduce machine learning.
Machine learning is useful for determining future data as it can detect current data automatically. This textbook covers a wide range of topics relating to the subject by going in-depth into each topic.
This includes fundamentals such as:
- Probability
- Optimization
- Linear algebra
- Conditional random fields
- L1 regularization
- Deep learning
- The latest developments in machine learning
The author uses an informal, accessible style to make it easy to understand these concepts. He uses pseudo-code for most of the algorithms in the text. Each topic is illustrated with color images and worked examples.
Undergraduate students with a background in introductory college math will find this textbook helpful, while beginner graduate students will find it instrumental for understanding machine learning.
- Author: Kevin P. Murphy
- Publisher: The MIT Press; 1 Edition (August 24, 2012)
- Pages: 1104 pages
5. Machine Learning for Absolute Beginners
As the name implies, Machine Learning for Absolute Beginners is perfect for the complete novice. The author explains key concepts in simple, easy to understand language for those without any prior experience in coding.
Visual examples and understandable explanations are used to present core algorithms so the novice can follow along with ease. It covers a wide range of topics, including:
Downloading free datasets
The machine learning libraries and tools needed
- Data scrubbing techniques
- Preparation of data for analysis
- Regression analysis
- Clustering, this includes k-nearest and k-means
- Neural Networks
- Bias/Variance, which is instrumental to the improvement of machine learning models
- Decision Trees for the decoding of classification
- Using Python to build a Machine Learning Model
This is the second edition and it covers quite a large number of topics that aren’t included in the first version. Most readers describe the book as a perfect starting point for beginners. However, once you are above this level, it might seem too elementary.
- Author: Oliver Theobald
- Publisher: Independently Published (January 1, 2018)
- Pages: 182 pages
6. Advances in Financial Machine Learning
Advances in Financial Machine Learning aims to describe how machine learning can be adopted to finance. Until machine learning was developed, only experts could perform certain tasks in this area, which created a number of limitations.
This textbook helps the reader see how the use of machine learning can transform investment. To this end, the reader is taught how to use big data in machine learning algorithms.
Other concepts taught in this text include:
- Conducting research on data using machine learning algorithms.
- Using supercomputing methods.
- Methods used in backtesting discoveries at the same time, avoiding any false positives.
Real-world problems are explained and solved using math and corresponding codes in clear examples in this book. It is a great tool for investment professionals who want to learn about groundbreaking tools for their profession.
- Author: Lopez de Prado, Marcos
- Publisher: Wiley, 1 Edition (February 21, 2018)
- Pages: 400 pages
7. Pattern Recognition and Machine Learning
This book is the first to present the Bayesian perspective, teaching the reader about approximate inference algorithms that allow for quick approximate answers in situations where it isn’t possible to get exact answers.
Probability distributions are described using graphical models, which puts it a step ahead of others in this field. These geometric illustrations and intuitions are strong features of this book which can serve as a foundation for many other courses.
The reader should have gained some knowledge in basic linear algebra and multivariate calculus before using this text. Experience with probabilities will be an added advantage. It is perfect for advanced undergraduates, researches, practitioners, and Ph.D. students.
- Author: Christopher D. Bishop
- Publisher: Springer (April 6, 2011)
- Pages: 738 pages
Conclusion
Any of these books on machine learning would be instrumental for both self-study and in classes. As you seek to learn more about this subject, these books will prove to be formidable companions.
